Brain
Contents
Brain#
This section presents results of brain MRI data. Below are quantitative T1 values computed using the MP2RAGE and the MTsat methods. These values are averaged within the gray matter and white matter masks.
Code imports#
# Python imports
from IPython.display import clear_output
from pathlib import Path
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 1)
# Import custom tools
from tools.data import Data
from tools.plot import Plot
from tools.stats import Stats
Download data#
data_type = 'brain'
release_version = 'latest'
dataset = Data(data_type)
dataset.download(release_version)
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Load data plot it#
qMRI Metrics#
dataset.load()
fig_gm = Plot(dataset, plot_name = 'brain-1')
fig_gm.title = 'Brain qMRI microstructure measures'
# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_gm.display('jupyter-book')
Statistics#
White Matter#
stats_wm = Stats(dataset)
stats_wm.build_df('WM')
stats_wm.build_stats_table()
display(stats_wm.stats_table)
| T1 (MP2RAGE) | T1 (MTsat) | MTR | MTsat | |
|---|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 2.3 | 0.6 | 1.7 |
| intrasubject COV std [%] | 0.2 | 0.8 | 0.1 | 0.5 |
| intersubject mean COV [%] | 1.9 | 3.5 | 0.4 | 2.2 |
Grey Matter#
stats_gm = Stats(dataset)
stats_gm.build_df('GM')
stats_gm.build_stats_table()
display(stats_gm.stats_table)
| T1 (MP2RAGE) | T1 (MTsat) | MTR | MTsat | |
|---|---|---|---|---|
| intrasubject COV mean [%] | 0.4 | 3.1 | 0.8 | 2.7 |
| intrasubject COV std [%] | 0.1 | 1.6 | 0.2 | 1.2 |
| intersubject mean COV [%] | 1.0 | 5.7 | 1.2 | 4.5 |
Diffusion#
data_type = 'brain-diffusion-cc'
release_version = 'latest'
dataset = Data(data_type)
dataset.download(release_version)
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dataset.load()
fig_diff = Plot(dataset, plot_name = 'brain-diff-cc')
fig_diff.title = 'Brain qMRI diffusion measures - corpus callosum'
fig_diff.display('jupyter-book')
Statistics#
Genu#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('genu')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.8 | 1.0 | 1.3 |
| intrasubject COV std [%] | 0.3 | 0.6 | 0.6 |
| intersubject mean COV [%] | 4.2 | 6.2 | 10.3 |
Body#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('body')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 0.7 | 0.7 |
| intrasubject COV std [%] | 0.2 | 0.2 | 0.3 |
| intersubject mean COV [%] | 3.8 | 3.0 | 6.2 |
Splenium#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('splenium')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 0.7 | 0.8 |
| intrasubject COV std [%] | 0.1 | 0.2 | 0.3 |
| intersubject mean COV [%] | 2.6 | 3.1 | 6.3 |